A complicating factor for selection is the influence of a genotype - environment (GE) interaction. The Bayesian approach is a tool to increase the efficiency of adaptability and stability methodologies. In this context, the objective of this study was to evaluate linear and bi-linear parameters of Additive Main Effects and Multiplicative Interaction (AMMI) analysis by the Bayesian approach for selection of food–type soybean genotypes in multi-environments trials. The grain yield of five lipoxygenase-free lines intended for human consumption of the soybean breeding program of the Londrina State University and of two commercial standards (BRS 257 and BMX Potencia RR) was evaluated in four counties of the State of Parana, Brazil, in the 2014/15, 2015/16 and 2016/17 growing seasons. Of the evaluated lines, only UEL 110 and UEL 122 had positive posterior genotypic effects, exceeding a probability of 95% of the commercial standard BRS 257. Only lines UEL 115 and UEL 123 did not contribute significantly to the GE interaction. Lines UEL 110 and UEL 122 proved adaptable to the largest number of environments with significant GE interaction and are therefore promising for the development of new food-type soybean cultivars. The use of AMMI1 (PC1 vs effects genotypes) shows results for stability of genotypes similar to AMMI2 (PC1 vs PC2) allowing a direct selection by the biplot for productivity and stability.